Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. ** We evaluate Snowflake prediction models with Transfer Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the SNOW stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold SNOW stock.**

**SNOW, Snowflake, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- How accurate is machine learning in stock market?
- What is Markov decision process in reinforcement learning?
- Can statistics predict the future?

## SNOW Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider Snowflake Stock Decision Process with Lasso Regression where A is the set of discrete actions of SNOW stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Lasso Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of SNOW stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## SNOW Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**SNOW Snowflake

**Time series to forecast n: 18 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold SNOW stock.**

**X axis: *Likelihood%** (The higher the percentage value, the more likely the event will occur.)

**Y axis: *Potential Impact%** (The higher the percentage value, the more likely the price will deviate.)

**Z axis (Yellow to Green): *Technical Analysis%**

## Conclusions

Snowflake assigned short-term Baa2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the SNOW stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold SNOW stock.**

### Financial State Forecast for SNOW Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Baa2 | B2 |

Operational Risk | 40 | 59 |

Market Risk | 76 | 33 |

Technical Analysis | 89 | 33 |

Fundamental Analysis | 78 | 52 |

Risk Unsystematic | 79 | 80 |

### Prediction Confidence Score

## References

- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
- J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28

## Frequently Asked Questions

Q: What is the prediction methodology for SNOW stock?A: SNOW stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Lasso Regression

Q: Is SNOW stock a buy or sell?

A: The dominant strategy among neural network is to Hold SNOW Stock.

Q: Is Snowflake stock a good investment?

A: The consensus rating for Snowflake is Hold and assigned short-term Baa2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of SNOW stock?

A: The consensus rating for SNOW is Hold.

Q: What is the prediction period for SNOW stock?

A: The prediction period for SNOW is (n+6 month)

- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)